Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations17290
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory168.0 B

Variable types

Numeric17
DateTime1
Categorical3

Alerts

bathrooms is highly overall correlated with bedrooms and 7 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
long is highly overall correlated with zipcodeHigh correlation
price is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
waterfront is highly imbalanced (93.2%)Imbalance
view is highly imbalanced (71.8%)Imbalance
sqft_basement has 10480 (60.6%) zerosZeros
yr_renovated has 16533 (95.6%) zerosZeros

Reproduction

Analysis started2025-10-02 20:00:03.166027
Analysis finished2025-10-02 20:00:37.956071
Duration34.79 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct17169
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5644764 × 109
Minimum1000102
Maximum9.895 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:38.140716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.2204911 × 108
Q12.1254 × 109
median3.9023003 × 109
Q37.2979251 × 109
95-th percentile9.290856 × 109
Maximum9.895 × 109
Range9.8939999 × 109
Interquartile range (IQR)5.172525 × 109

Descriptive statistics

Standard deviation2.864976 × 109
Coefficient of variation (CV)0.62766806
Kurtosis-1.2463349
Mean4.5644764 × 109
Median Absolute Deviation (MAD)2.3772411 × 109
Skewness0.25565765
Sum7.8919796 × 1013
Variance8.2080876 × 1018
MonotonicityNot monotonic
2025-10-02T22:00:38.428429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3020003752
 
< 0.1%
75040213102
 
< 0.1%
42223100102
 
< 0.1%
27676036122
 
< 0.1%
50830003752
 
< 0.1%
12502011652
 
< 0.1%
94071107102
 
< 0.1%
32623009402
 
< 0.1%
92224006052
 
< 0.1%
78880003902
 
< 0.1%
Other values (17159)17270
99.9%
ValueCountFrequency (%)
10001022
< 0.1%
12000191
< 0.1%
12000211
< 0.1%
28000311
< 0.1%
36000571
< 0.1%
36000721
< 0.1%
38000081
< 0.1%
52000871
< 0.1%
62000171
< 0.1%
72000801
< 0.1%
ValueCountFrequency (%)
98950000401
< 0.1%
98423005401
< 0.1%
98423004851
< 0.1%
98423000361
< 0.1%
98393011651
< 0.1%
98393010601
< 0.1%
98393010551
< 0.1%
98393008751
< 0.1%
98393007751
< 0.1%
98393005451
< 0.1%

date
Date

Distinct369
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size135.2 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-02T22:00:38.675548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:38.901530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation 

Distinct3562
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean542799.84
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:39.123698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210500
Q1324000
median453000
Q3647500
95-th percentile1175000
Maximum7700000
Range7625000
Interquartile range (IQR)323500

Descriptive statistics

Standard deviation372439.03
Coefficient of variation (CV)0.68614432
Kurtosis36.366127
Mean542799.84
Median Absolute Deviation (MAD)152000
Skewness4.1293833
Sum9.3850092 × 109
Variance1.3871083 × 1011
MonotonicityNot monotonic
2025-10-02T22:00:39.338823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000134
 
0.8%
450000131
 
0.8%
550000127
 
0.7%
425000124
 
0.7%
500000124
 
0.7%
325000118
 
0.7%
375000113
 
0.7%
400000112
 
0.6%
250000107
 
0.6%
650000102
 
0.6%
Other values (3552)16098
93.1%
ValueCountFrequency (%)
750001
< 0.1%
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
820001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850002
< 0.1%
865001
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
53500001
< 0.1%
53000001
< 0.1%
51108001
< 0.1%
46680001
< 0.1%
45000001
< 0.1%
44890001
< 0.1%
40000001
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.3680495
Minimum0
Maximum33
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:39.493345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94311768
Coefficient of variation (CV)0.28001894
Kurtosis57.730327
Mean3.3680495
Median Absolute Deviation (MAD)1
Skewness2.2710443
Sum58210
Variance0.88947095
MonotonicityNot monotonic
2025-10-02T22:00:39.625309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
37830
45.3%
45437
31.4%
22257
 
13.1%
51307
 
7.6%
6225
 
1.3%
1166
 
1.0%
732
 
0.2%
011
 
0.1%
88
 
< 0.1%
95
 
< 0.1%
Other values (3)5
 
< 0.1%
(Missing)7
 
< 0.1%
ValueCountFrequency (%)
011
 
0.1%
1166
 
1.0%
22257
 
13.1%
37830
45.3%
45437
31.4%
51307
 
7.6%
6225
 
1.3%
732
 
0.2%
88
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
95
 
< 0.1%
88
 
< 0.1%
732
 
0.2%
6225
 
1.3%
51307
 
7.6%
45437
31.4%
37830
45.3%

bathrooms
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1182909
Minimum0
Maximum8
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:39.783333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.77224948
Coefficient of variation (CV)0.36456252
Kurtosis1.3422932
Mean2.1182909
Median Absolute Deviation (MAD)0.5
Skewness0.51556976
Sum36625.25
Variance0.59636925
MonotonicityNot monotonic
2025-10-02T22:00:39.929540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.54309
24.9%
13070
17.8%
1.752438
14.1%
2.251609
 
9.3%
21549
 
9.0%
1.51147
 
6.6%
2.75972
 
5.6%
3.5610
 
3.5%
3589
 
3.4%
3.25468
 
2.7%
Other values (20)529
 
3.1%
ValueCountFrequency (%)
09
 
0.1%
0.53
 
< 0.1%
0.7557
 
0.3%
13070
17.8%
1.258
 
< 0.1%
1.51147
 
6.6%
1.752438
14.1%
21549
 
9.0%
2.251609
 
9.3%
2.54309
24.9%
ValueCountFrequency (%)
82
 
< 0.1%
7.751
 
< 0.1%
7.51
 
< 0.1%
6.752
 
< 0.1%
6.52
 
< 0.1%
6.252
 
< 0.1%
63
 
< 0.1%
5.751
 
< 0.1%
5.59
0.1%
5.258
< 0.1%

sqft_living
Real number (ℝ)

High correlation 

Distinct942
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2081.4167
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:40.117635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile930
Q11420
median1920
Q32550
95-th percentile3780
Maximum13540
Range13250
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation923.24458
Coefficient of variation (CV)0.44356547
Kurtosis5.5925159
Mean2081.4167
Median Absolute Deviation (MAD)550
Skewness1.49953
Sum35987695
Variance852380.55
MonotonicityNot monotonic
2025-10-02T22:00:40.346734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400118
 
0.7%
1440112
 
0.6%
1800110
 
0.6%
1300106
 
0.6%
1010103
 
0.6%
1560101
 
0.6%
1540100
 
0.6%
166098
 
0.6%
132096
 
0.6%
182096
 
0.6%
Other values (932)16250
94.0%
ValueCountFrequency (%)
2901
< 0.1%
3801
< 0.1%
3902
< 0.1%
4201
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
4701
< 0.1%
4802
< 0.1%
4901
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
80001
< 0.1%
78801
< 0.1%

sqft_lot
Real number (ℝ)

High correlation 

Distinct8431
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15241.884
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:40.568127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1750
Q15033.25
median7588.5
Q310713
95-th percentile43560
Maximum1651359
Range1650839
Interquartile range (IQR)5679.75

Descriptive statistics

Standard deviation42270.28
Coefficient of variation (CV)2.7732975
Kurtosis277.26071
Mean15241.884
Median Absolute Deviation (MAD)2626.5
Skewness12.86597
Sum2.6353218 × 108
Variance1.7867766 × 109
MonotonicityNot monotonic
2025-10-02T22:00:40.793081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000297
 
1.7%
6000243
 
1.4%
4000200
 
1.2%
7200176
 
1.0%
4800100
 
0.6%
450095
 
0.5%
750084
 
0.5%
360084
 
0.5%
840082
 
0.5%
960081
 
0.5%
Other values (8421)15848
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6751
< 0.1%
6761
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9822781
< 0.1%
9204231
< 0.1%
8816541
< 0.1%
8712002
< 0.1%
8433091
< 0.1%
7156901
< 0.1%
6412031
< 0.1%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4980625
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:40.965207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54260257
Coefficient of variation (CV)0.3622029
Kurtosis-0.46267765
Mean1.4980625
Median Absolute Deviation (MAD)0.5
Skewness0.62117792
Sum25901.5
Variance0.29441755
MonotonicityNot monotonic
2025-10-02T22:00:41.085584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
18496
49.1%
26588
38.1%
1.51549
 
9.0%
3513
 
3.0%
2.5137
 
0.8%
3.57
 
< 0.1%
ValueCountFrequency (%)
18496
49.1%
1.51549
 
9.0%
26588
38.1%
2.5137
 
0.8%
3513
 
3.0%
3.57
 
< 0.1%
ValueCountFrequency (%)
3.57
 
< 0.1%
3513
 
3.0%
2.5137
 
0.8%
26588
38.1%
1.51549
 
9.0%
18496
49.1%

waterfront
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.2 KiB
0
17149 
1
 
141

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17290
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
017149
99.2%
1141
 
0.8%

Length

2025-10-02T22:00:41.234805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T22:00:41.352556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
017149
99.2%
1141
 
0.8%

Most occurring characters

ValueCountFrequency (%)
017149
99.2%
1141
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
017149
99.2%
1141
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
017149
99.2%
1141
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
017149
99.2%
1141
 
0.8%

view
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.2 KiB
0
15557 
2
 
788
3
 
406
1
 
278
4
 
261

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17290
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
015557
90.0%
2788
 
4.6%
3406
 
2.3%
1278
 
1.6%
4261
 
1.5%

Length

2025-10-02T22:00:41.491535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T22:00:41.604596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
015557
90.0%
2788
 
4.6%
3406
 
2.3%
1278
 
1.6%
4261
 
1.5%

Most occurring characters

ValueCountFrequency (%)
015557
90.0%
2788
 
4.6%
3406
 
2.3%
1278
 
1.6%
4261
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
015557
90.0%
2788
 
4.6%
3406
 
2.3%
1278
 
1.6%
4261
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
015557
90.0%
2788
 
4.6%
3406
 
2.3%
1278
 
1.6%
4261
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
015557
90.0%
2788
 
4.6%
3406
 
2.3%
1278
 
1.6%
4261
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.2 KiB
3
11259 
4
4513 
5
1354 
2
 
139
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17290
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
311259
65.1%
44513
26.1%
51354
 
7.8%
2139
 
0.8%
125
 
0.1%

Length

2025-10-02T22:00:41.748205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T22:00:41.874374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
311259
65.1%
44513
26.1%
51354
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring characters

ValueCountFrequency (%)
311259
65.1%
44513
26.1%
51354
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
311259
65.1%
44513
26.1%
51354
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
311259
65.1%
44513
26.1%
51354
 
7.8%
2139
 
0.8%
125
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
311259
65.1%
44513
26.1%
51354
 
7.8%
2139
 
0.8%
125
 
0.1%

grade
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6600347
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:42.003315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1764281
Coefficient of variation (CV)0.15358
Kurtosis1.2207208
Mean7.6600347
Median Absolute Deviation (MAD)1
Skewness0.76483861
Sum132442
Variance1.3839831
MonotonicityNot monotonic
2025-10-02T22:00:42.147532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
77137
41.3%
84910
28.4%
92090
 
12.1%
61623
 
9.4%
10896
 
5.2%
11331
 
1.9%
5196
 
1.1%
1268
 
0.4%
423
 
0.1%
1312
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
423
 
0.1%
5196
 
1.1%
61623
 
9.4%
77137
41.3%
84910
28.4%
92090
 
12.1%
10896
 
5.2%
11331
 
1.9%
ValueCountFrequency (%)
1312
 
0.1%
1268
 
0.4%
11331
 
1.9%
10896
 
5.2%
92090
 
12.1%
84910
28.4%
77137
41.3%
61623
 
9.4%
5196
 
1.1%
423
 
0.1%

sqft_above
Real number (ℝ)

High correlation 

Distinct853
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.2996
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:42.339516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile840
Q11190
median1560
Q32210
95-th percentile3415.5
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation831.88962
Coefficient of variation (CV)0.4651847
Kurtosis3.5376648
Mean1788.2996
Median Absolute Deviation (MAD)450
Skewness1.4686636
Sum30919700
Variance692040.33
MonotonicityNot monotonic
2025-10-02T22:00:42.540533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010172
 
1.0%
1300162
 
0.9%
1200160
 
0.9%
1340147
 
0.9%
1400147
 
0.9%
1220146
 
0.8%
1180142
 
0.8%
1140140
 
0.8%
1060139
 
0.8%
1320138
 
0.8%
Other values (843)15797
91.4%
ValueCountFrequency (%)
2901
 
< 0.1%
3801
 
< 0.1%
3902
< 0.1%
4201
 
< 0.1%
4301
 
< 0.1%
4401
 
< 0.1%
4601
 
< 0.1%
4701
 
< 0.1%
4803
< 0.1%
4902
< 0.1%
ValueCountFrequency (%)
94101
< 0.1%
88601
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%
76801
< 0.1%
73201
< 0.1%
67201
< 0.1%
66401
< 0.1%
65301
< 0.1%

sqft_basement
Real number (ℝ)

Zeros 

Distinct282
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.11712
Minimum0
Maximum4820
Zeros10480
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:42.719508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3570
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)570

Descriptive statistics

Standard deviation443.63039
Coefficient of variation (CV)1.5134919
Kurtosis2.8444243
Mean293.11712
Median Absolute Deviation (MAD)0
Skewness1.5809084
Sum5067995
Variance196807.92
MonotonicityNot monotonic
2025-10-02T22:00:42.938865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010480
60.6%
600180
 
1.0%
700179
 
1.0%
500177
 
1.0%
800157
 
0.9%
400145
 
0.8%
1000122
 
0.7%
900114
 
0.7%
300109
 
0.6%
72087
 
0.5%
Other values (272)5540
32.0%
ValueCountFrequency (%)
010480
60.6%
101
 
< 0.1%
201
 
< 0.1%
402
 
< 0.1%
5010
 
0.1%
607
 
< 0.1%
651
 
< 0.1%
707
 
< 0.1%
8019
 
0.1%
9018
 
0.1%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%
28501
< 0.1%
27301
< 0.1%
27201
< 0.1%
26001
< 0.1%
25901
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9642
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:43.121532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.485431
Coefficient of variation (CV)0.014959902
Kurtosis-0.65930366
Mean1970.9642
Median Absolute Deviation (MAD)23
Skewness-0.47006591
Sum34077971
Variance869.39064
MonotonicityNot monotonic
2025-10-02T22:00:43.816496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014450
 
2.6%
2006361
 
2.1%
2005358
 
2.1%
2003347
 
2.0%
2004341
 
2.0%
1977341
 
2.0%
2007328
 
1.9%
1968315
 
1.8%
1978309
 
1.8%
2008295
 
1.7%
Other values (106)13845
80.1%
ValueCountFrequency (%)
190073
0.4%
190123
 
0.1%
190222
 
0.1%
190341
0.2%
190440
0.2%
190557
0.3%
190683
0.5%
190755
0.3%
190863
0.4%
190976
0.4%
ValueCountFrequency (%)
201535
 
0.2%
2014450
2.6%
2013168
 
1.0%
2012145
 
0.8%
2011107
 
0.6%
2010114
 
0.7%
2009189
1.1%
2008295
1.7%
2007328
1.9%
2006361
2.1%

yr_renovated
Real number (ℝ)

Zeros 

Distinct67
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.387681
Minimum0
Maximum2015
Zeros16533
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:44.000403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation408.4174
Coefficient of variation (CV)4.6736267
Kurtosis17.897003
Mean87.387681
Median Absolute Deviation (MAD)0
Skewness4.4602088
Sum1510933
Variance166804.77
MonotonicityNot monotonic
2025-10-02T22:00:44.181826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016533
95.6%
201473
 
0.4%
201333
 
0.2%
200731
 
0.2%
200030
 
0.2%
200528
 
0.2%
200328
 
0.2%
200422
 
0.1%
200922
 
0.1%
199020
 
0.1%
Other values (57)470
 
2.7%
ValueCountFrequency (%)
016533
95.6%
19401
 
< 0.1%
19441
 
< 0.1%
19453
 
< 0.1%
19462
 
< 0.1%
19501
 
< 0.1%
19511
 
< 0.1%
19533
 
< 0.1%
19541
 
< 0.1%
19552
 
< 0.1%
ValueCountFrequency (%)
201513
 
0.1%
201473
0.4%
201333
0.2%
201211
 
0.1%
201111
 
0.1%
201015
 
0.1%
200922
 
0.1%
200816
 
0.1%
200731
0.2%
200615
 
0.1%

zipcode
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.935
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:44.368700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.505656
Coefficient of variation (CV)0.00054554223
Kurtosis-0.85391584
Mean98077.935
Median Absolute Deviation (MAD)42
Skewness0.40020502
Sum1.6957675 × 109
Variance2862.8552
MonotonicityNot monotonic
2025-10-02T22:00:44.573208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103485
 
2.8%
98038478
 
2.8%
98052458
 
2.6%
98115458
 
2.6%
98117449
 
2.6%
98034437
 
2.5%
98042422
 
2.4%
98118404
 
2.3%
98006404
 
2.3%
98133394
 
2.3%
Other values (60)12901
74.6%
ValueCountFrequency (%)
98001299
1.7%
98002152
 
0.9%
98003224
1.3%
98004264
1.5%
98005135
 
0.8%
98006404
2.3%
98007111
 
0.6%
98008230
1.3%
9801083
 
0.5%
98011153
 
0.9%
ValueCountFrequency (%)
98199259
1.5%
98198227
1.3%
98188103
 
0.6%
98178197
1.1%
98177208
1.2%
98168204
1.2%
98166203
1.2%
98155360
2.1%
9814847
 
0.3%
98146210
1.2%

lat
Real number (ℝ)

Distinct4846
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560451
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:44.755280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.309645
Q147.4724
median47.5731
Q347.6779
95-th percentile47.7494
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2055

Descriptive statistics

Standard deviation0.1386244
Coefficient of variation (CV)0.0029146989
Kurtosis-0.65607053
Mean47.560451
Median Absolute Deviation (MAD)0.1038
Skewness-0.50058647
Sum822320.2
Variance0.019216723
MonotonicityNot monotonic
2025-10-02T22:00:44.933165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.671116
 
0.1%
47.549115
 
0.1%
47.532214
 
0.1%
47.684414
 
0.1%
47.637414
 
0.1%
47.690414
 
0.1%
47.68614
 
0.1%
47.664713
 
0.1%
47.542713
 
0.1%
47.684213
 
0.1%
Other values (4836)17150
99.2%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.17641
< 0.1%
47.17751
< 0.1%
47.17761
< 0.1%
47.17951
< 0.1%
47.18081
< 0.1%
47.1841
< 0.1%
47.18531
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77752
< 0.1%
47.77741
 
< 0.1%
47.77723
< 0.1%
47.77712
< 0.1%
47.7772
< 0.1%
47.77693
< 0.1%
47.77674
< 0.1%
47.77664
< 0.1%
47.77653
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct723
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21419
Minimum-122.514
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative17290
Negative (%)100.0%
Memory size135.2 KiB
2025-10-02T22:00:45.093177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-122.514
5-th percentile-122.387
Q1-122.328
median-122.2315
Q3-122.125
95-th percentile-121.98
Maximum-121.315
Range1.199
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14074546
Coefficient of variation (CV)-0.0011516294
Kurtosis1.0205698
Mean-122.21419
Median Absolute Deviation (MAD)0.1015
Skewness0.88156052
Sum-2113083.4
Variance0.019809284
MonotonicityNot monotonic
2025-10-02T22:00:45.278486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.2992
 
0.5%
-122.36288
 
0.5%
-122.385
 
0.5%
-122.37282
 
0.5%
-122.29180
 
0.5%
-122.30478
 
0.5%
-122.37578
 
0.5%
-122.29977
 
0.4%
-122.28877
 
0.4%
-122.28477
 
0.4%
Other values (713)16476
95.3%
ValueCountFrequency (%)
-122.5141
< 0.1%
-122.5121
< 0.1%
-122.5112
< 0.1%
-122.5091
< 0.1%
-122.5071
< 0.1%
-122.5061
< 0.1%
-122.5052
< 0.1%
-122.5042
< 0.1%
-122.5032
< 0.1%
-122.5021
< 0.1%
ValueCountFrequency (%)
-121.3152
< 0.1%
-121.3191
< 0.1%
-121.3211
< 0.1%
-121.3522
< 0.1%
-121.3591
< 0.1%
-121.3641
< 0.1%
-121.4021
< 0.1%
-121.4031
< 0.1%
-121.4051
< 0.1%
-121.4171
< 0.1%

sqft_living15
Real number (ℝ)

High correlation 

Distinct713
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1987.4751
Minimum399
Maximum6110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:45.478589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6110
Range5711
Interquartile range (IQR)870

Descriptive statistics

Standard deviation686.25538
Coefficient of variation (CV)0.34529006
Kurtosis1.5683662
Mean1987.4751
Median Absolute Deviation (MAD)410
Skewness1.1057171
Sum34363444
Variance470946.45
MonotonicityNot monotonic
2025-10-02T22:00:45.645496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540160
 
0.9%
1440158
 
0.9%
1560153
 
0.9%
1500149
 
0.9%
1720140
 
0.8%
1580139
 
0.8%
1800134
 
0.8%
1610132
 
0.8%
1760130
 
0.8%
1470129
 
0.7%
Other values (703)15866
91.8%
ValueCountFrequency (%)
3991
 
< 0.1%
4602
 
< 0.1%
6202
 
< 0.1%
6901
 
< 0.1%
7002
 
< 0.1%
7102
 
< 0.1%
7201
 
< 0.1%
7406
< 0.1%
7502
 
< 0.1%
7602
 
< 0.1%
ValueCountFrequency (%)
61101
 
< 0.1%
57905
< 0.1%
56001
 
< 0.1%
55001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
53301
 
< 0.1%
52201
 
< 0.1%
52001
 
< 0.1%
51101
 
< 0.1%

sqft_lot15
Real number (ℝ)

High correlation 

Distinct7569
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12834.458
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.2 KiB
2025-10-02T22:00:45.835557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1929.15
Q15100
median7619
Q310099.5
95-th percentile37038.4
Maximum871200
Range870549
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation27766.935
Coefficient of variation (CV)2.1634676
Kurtosis162.92897
Mean12834.458
Median Absolute Deviation (MAD)2518
Skewness9.8139161
Sum2.2190779 × 108
Variance7.7100266 × 108
MonotonicityNot monotonic
2025-10-02T22:00:46.033357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000348
 
2.0%
4000293
 
1.7%
6000249
 
1.4%
7200165
 
1.0%
4800115
 
0.7%
7500113
 
0.7%
360095
 
0.5%
840093
 
0.5%
450090
 
0.5%
510082
 
0.5%
Other values (7559)15647
90.5%
ValueCountFrequency (%)
6511
 
< 0.1%
6591
 
< 0.1%
6601
 
< 0.1%
7481
 
< 0.1%
7504
< 0.1%
7551
 
< 0.1%
7581
 
< 0.1%
7881
 
< 0.1%
8091
 
< 0.1%
8102
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
8581321
< 0.1%
5606171
< 0.1%
4347281
< 0.1%
4255811
< 0.1%
4229671
< 0.1%
4119621
< 0.1%
3920402
< 0.1%
3802791
< 0.1%
3600001
< 0.1%

Interactions

2025-10-02T22:00:34.338989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:04.164144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:05.697276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:07.356260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:08.796760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:10.451659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:11.949249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:13.555933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:14.950157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:16.585746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:17.955074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:19.381360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:21.141906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:22.654656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:25.042049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:28.033479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:31.325369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:34.496139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:04.251595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-02T22:00:33.467937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:36.557828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:05.361177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:06.985649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:08.453539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:10.108570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:11.584255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:13.212445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:14.626696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:16.266481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:17.630234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-02T22:00:20.574293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:22.306977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:24.325003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:27.347539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:30.691343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:33.658567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:36.722577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:05.451914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:07.078368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:08.553606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:10.200769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:11.672668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:13.297124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:14.709688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:16.350786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:17.705485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:19.132305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:20.659346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:22.392902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:24.512021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:27.526985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:30.851146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:33.819508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:36.874379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:05.528790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:07.172170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:08.638348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:10.286901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:11.762320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:13.382464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:14.792803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:16.431304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:17.783719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:19.220182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:20.991972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:22.487413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:24.692376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:27.705220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:31.008717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:33.991458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:37.061853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:05.610481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:07.260990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:08.719160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:10.370423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:11.858316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:13.465297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:14.869869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:16.509486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:17.866477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:19.303284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:21.065580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:22.570135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:24.864725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:27.876274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:31.164807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T22:00:34.173628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-02T22:00:46.197822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.5250.1310.5440.6590.0170.0110.2600.5000.6900.1950.7440.5660.0670.0610.1200.1210.5620.049-0.203
bedrooms0.5251.0000.0250.2280.3830.007-0.0190.1960.3530.5440.2370.6530.4470.2150.1990.0380.0000.1800.024-0.171
condition0.1310.0251.0000.1820.1640.0290.0550.0840.0200.1090.0960.0600.0610.0110.0100.0260.0240.2510.0680.076
floors0.5440.2280.1821.0000.5030.0210.0270.1430.3240.597-0.2750.3960.298-0.236-0.2340.0250.0250.5500.020-0.057
grade0.6590.3830.1640.5031.0000.0240.1040.2230.6570.7090.0950.7140.6560.1490.1550.1460.1270.5020.021-0.180
id0.0170.0070.0290.0210.0241.000-0.0040.0100.0070.0070.0020.0040.000-0.111-0.1100.0280.0110.025-0.016-0.007
lat0.011-0.0190.0550.0270.104-0.0041.000-0.1510.451-0.0260.1150.0310.027-0.129-0.1200.0680.034-0.1220.0260.255
long0.2600.1960.0840.1430.2230.010-0.1511.0000.0600.387-0.1940.2880.3820.3780.3820.0780.0890.410-0.073-0.583
price0.5000.3530.0200.3240.6570.0070.4510.0601.0000.5430.2540.6450.5720.0710.0600.2160.3360.1010.106-0.007
sqft_above0.6900.5440.1090.5970.7090.007-0.0260.3870.5431.000-0.1610.8440.6940.2770.2590.0940.0990.4660.040-0.280
sqft_basement0.1950.2370.096-0.2750.0950.0020.115-0.1940.254-0.1611.0000.3320.1370.0370.0320.1640.144-0.1770.0620.109
sqft_living0.7440.6530.0600.3960.7140.0040.0310.2880.6450.8440.3321.0000.7460.3080.2890.1570.1560.3470.060-0.211
sqft_living150.5660.4470.0610.2980.6560.0000.0270.3820.5720.6940.1370.7461.0000.3650.3720.1510.0960.328-0.001-0.288
sqft_lot0.0670.2150.011-0.2360.149-0.111-0.1290.3780.0710.2770.0370.3080.3651.0000.9240.0460.015-0.0400.012-0.329
sqft_lot150.0610.1990.010-0.2340.155-0.110-0.1200.3820.0600.2590.0320.2890.3720.9241.0000.0370.000-0.0170.010-0.336
view0.1200.0380.0260.0250.1460.0280.0680.0780.2160.0940.1640.1570.1510.0460.0371.0000.6140.0420.1110.075
waterfront0.1210.0000.0240.0250.1270.0110.0340.0890.3360.0990.1440.1560.0960.0150.0000.6141.0000.0330.0920.082
yr_built0.5620.1800.2510.5500.5020.025-0.1220.4100.1010.466-0.1770.3470.328-0.040-0.0170.0420.0331.000-0.216-0.312
yr_renovated0.0490.0240.0680.0200.021-0.0160.026-0.0730.1060.0400.0620.060-0.0010.0120.0100.1110.092-0.2161.0000.062
zipcode-0.203-0.1710.076-0.057-0.180-0.0070.255-0.583-0.007-0.2800.109-0.211-0.288-0.329-0.3360.0750.082-0.3120.0621.000

Missing values

2025-10-02T22:00:37.347122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-02T22:00:37.699133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0510040266820150218T000000495000.03.01.00157055101.000471070500194009811547.6942-122.31917706380
1785656048020140808T000000635000.03.02.501780110001.000481210570198009800647.5574-122.14923109700
2287290001020150414T000000382500.03.01.50109098621.0003810900198709807447.6256-122.03617109862
3321690007020140617T000000382500.04.02.50221070792.0003822100199309803147.4206-122.18319707000
497600079020141020T000000670000.03.02.50180047632.000371240560198509811947.6460-122.36217904763
5681510008520141224T0000001001000.04.02.00310080001.5005720401060193909810347.6852-122.32916504000
6723755002020140703T0000001100000.04.03.755070601232.00031150700200009805347.6567-122.0044920101930
7123100031020140812T000000713000.01.01.00118040001.50248840340191009811847.5561-122.26614204000
8866326003020141118T000000416000.03.02.50180053722.0003818000198709803447.7188-122.17716506014
9116000011520150304T000000401000.04.01.753010125231.0003817801230195209812547.7070-122.31620407560
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
17280673870000520140529T000000395000.02.01.00132018241.5004613200190909814447.5850-122.29413204000
17281290220091520141125T000000675000.03.01.75213044001.000371430700192209810247.6417-122.32517103300
17282302290007020140929T000000348000.03.02.00236061451.0003823600201109803047.3564-122.19823045880
17283691770019520140728T000000585000.03.01.75148048002.000471140340194409819947.6567-122.39718104800
17284110500040220141028T000000630000.04.03.00364050962.000382740900201009811847.5428-122.27019109189
17285232206901020141007T0000001180000.05.05.003960940892.00031039600199809803847.3800-122.011224064468
17286211470036820141118T000000299000.02.02.50140012622.000381160240200809810647.5342-122.34910601524
17287546950120020140820T000000431000.03.02.252360149501.0004923600197809804247.3856-122.158272014388
17288375160279720140702T000000411000.04.02.002370766652.0004823700197809800147.2831-122.279211019334
17289403860026020140922T000000699900.04.02.252380162361.000371540840196109800847.6126-122.12022308925